Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling.

Emerging spatial profiling technology has enabled high-plex molecular profiling in biological tissues, preserving the spatial and morphological context of gene expression. Here, we describe expanding the chemistry for the Digital Spatial Profiling platform to quantify whole transcriptomes in human and mouse tissues using a wide range of spatial profiling strategies and sample types. We designed multiplexed in situ hybridization probes targeting the protein-coding genes of the human and mouse transcriptomes, referred to as the human or mouse Whole Transcriptome Atlas (WTA). Human and mouse WTAs were validated in cell lines for concordance with orthogonal gene expression profiling methods in regions ranging from ∼10-500 cells. By benchmarking against bulk RNA-seq and fluorescence in situ hybridization, we show robust transcript detection down to ∼100 transcripts per region. To assess the performance of WTA across tissue and sample types, we applied WTA to biological questions in cancer, molecular pathology, and developmental biology. Spatial profiling with WTA detected expected gene expression differences between tumor and tumor microenvironment, identified disease-specific gene expression heterogeneity in histological structures of the human kidney, and comprehensively mapped transcriptional programs in anatomical substructures of nine organs in the developing mouse embryo. Digital Spatial Profiling technology with the WTA assays provides a flexible method for spatial whole transcriptome profiling applicable to diverse tissue types and biological contexts.

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

GeneSpeed: protein domain organization of the transcriptome.

The GeneSpeed database (http://genespeed.uchsc.edu/) is an online database and resource tool facilitating the detailed study of protein domain homology in the transcriptomes of Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. The population schema for the GeneSpeed database takes advantage of HOWARD parallel cluster technology (http://www.massivelyparallel.com/) and performs exhaustive tBLASTn searches covering all pre-assigned PFAM domain classes in all species (currently 7973 domain families) against the respective Unigene EST databases of the selected four transcriptomes. The resulting database provides a complete annotation of presumed protein domain presence for each Unigene cluster. To complement this domain annotation we have also performed a custom transcription factor-family curation of all Pfam domains, incorporated the Gene Ontology classifications for these domains as well as integrated the Novartis SymAtlas2 dataset for both human and mouse which provides rapid and easy access to tissue-based expression analysis. Consequently, the GeneSpeed database provides the user with the capability to browse or search the database by any of these specialized criteria as well as more traditional means (gene identifier, gene symbol, etc.), thereby enabling a supervised analysis of gene families through a top-down hierarchical basis defined by domain content, all directly linked to an optimized gene expression dataset.

GeneSpeed Beta Cell: an online genomics data repository and analysis resource tailored for the islet cell biologist.

OBJECTIVE: We here describe the development of a freely available online database resource, GeneSpeed Beta Cell, which has been created for the pancreatic islet and pancreatic developmental biology investigator community. RESEARCH DESIGN AND METHODS: We have developed GeneSpeed Beta Cell as a separate component of the GeneSpeed database, providing a genomics-type data repository of pancreas and islet-relevant datasets interlinked with the domain-oriented GeneSpeed database. RESULTS: GeneSpeed Beta Cell allows the query of multiple published and unpublished select genomics datasets in a simultaneous fashion (multiexperiment viewing) and is capable of defining intersection results from precomputed analysis of such datasets (multidimensional querying). Combined with the protein-domain categorization/assembly toolbox provided by the GeneSpeed database, the user is able to define spatial expression constraints of select gene lists in a relatively rigid fashion within the pancreatic expression space. We provide several demonstration case studies of relevance to islet cell biology and development of the pancreas that provide novel insight into islet biology. CONCLUSIONS: The combination of an exhaustive domain-based compilation of the transcriptome with gene array data of interest to the islet biologist affords novel methods for multidimensional querying between individual datasets in a rapid fashion, presently not available elsewhere.